Geospatial Planning for Least-Cost Electrification in Developing Countries

This paper presents two innovative procedures developed for rural electrification planning. To address the challenges of processing vast geospatial data, handling complex and computationally intensive network design, and making detailed yet accessible economic assessments, this work introduces a Buffering plugin for community identification and a Grid Routing and Cost Allocation plugin for network design and economic assessment, both integrated into the open-source QGIS platform. The first enables the identification of potential electrification zones through dual methodologies, while the second introduces three key processes: hierarchical clustering, a modified minimum spanning tree, and a novel cost allocation methodology that provides village-specific LCOE calculations. Testing in Zambia has proven that this approach is not only effective but also—compared to existing tools—offers significant advantages in terms of computational efficiency and accessibility, while providing practical solutions to large-scale challenges. This synergistic approach enables planners to move from granular geospatial data to actionable electrification decisions through a streamlined process. The analysis covered over 3 million buildings, grouped into 162,142 settlement clusters, and subsequently determined optimal electrification strategies for 3025 villages—40.4% connected to grid extensions and 59.6% to mini-grids—serving a total population of 18 million people.